Bayesian Inference for Four tops at the LHC

Four top production is one of the last benchmarks of the SM
explored at the LHC, and thus the intersection of state of the art
experimental techniques and theoretical calculations. In this talk, we give
a brief review of the main problems one faces when trying to disentangle
signal from background in such a complex final state. We then propose a
relatively simple probabilistic mixture model where the Monte Carlo
simulations play the role of prior knowledge. Using a simulated dataset with
"bad" priors and known numerical inference techniques, we are able to
correct the initial modelling on the data to a certain degree. This in turn
opens the door for a reduction of simulation systematics and a higher
sensitivity to possible BSM effects.